Download - 9.913 Pattern Recognition for Vision Class9 - Object Detection and Recognition Bernd Heisele
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9.913 Pattern Recognition for Vision
Class9 - Object Detection and Recognition
Bernd Heisele
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Outline
• Object Detection• Object Recognition
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Object Detection
• Task: Given an input image, determine if there are objects of a given class in the image and where they are located.
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Face Detection System Architecture
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Testing
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Image Features
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ROC for Image Features
Gray
Gray + Haar
Haar
Gray + Grad
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Positive Training Data
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Real vs. Synthetic
Real
Synthetic
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ROC for Classifiers
LDA
Linear SVM
Poly2
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Global vs. Components
(Whole Face)
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Component-based Detection
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Some Examples
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ROC Component vs. Global
• About 40000 faces
• 68 people
• 13 poses
• 43 illuminations condition
• CMU PIE database
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Training on Faces
Positive
Facial Negative
Non-facial Negative
Use the remainder of the face in the negative training set
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Training on Faces
Red: Trained on facial and non-facial negative set.
Blue: Trained only on non-facial negative set.
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Pair-wise Biasing
Often, many components classify correctly, with only a few errors.
Use the pair-wise relative position information from training data to bias the result image.
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Pair-wise Biasing
Result Images
Biased Results
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ROC Pair-wise Biasing
Red: Trained on facial and non-facial negative set.
Blue: Trained only on non-facial negative set.
Dashed: Biasing andtrained on facial and non-facial negative set.
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Pedestrian Detection
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Object Recognition
• Task: Given an image of and object of a particular class identify which exemplar it is.
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Recognition System Architecture
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Multi-class Classification with SVM
Training: N (N-1) / 2Classification: N - 1
Training: NClassification: N
The two different architecture has similar performance!!
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Global Approach
1. Detect and extract face
2. Feed gray values of extracted face into N SVMs
3. Classify based on maximum output
Each SVM is one vs. all approach
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Global Approach with Clustering
T1. Partition training images of each person into viewpoint- specific clusters
T2. Train a SVM on each cluster.
R1. Detect and extract face
R2. Feed extracted face to all SVMs
R3. Take maximum over all SVM outputs
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Component-based Approach
1. Detect face and extract components
2. Combine gray values of components to a feature vector , and feed to the N SVMs
3. Take maximum over all SVM outputs
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ROC Component vs. Global Recognition
• Trained and tested on frontal and rotated faces.